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Back-propagation network by backing error and adopting coefficient overcome this limitation using hidden layers. Backpropagation network is also called Multilayer Perceptron Network.

Such error is determined for each neuron, and applied for adopting weighting coefficient and activation value. It is learning (training) and validating of the network.

The weighting coefficient are calculated

through Equation 3 and 4.

Backpropagation (disadvantages) behaviour through– the most used paradigm, but often characterised with long lasting training. Simple (basic) neuron architecture recognize inputs behaviour through finding linearity (it is perceptron concept). It resulted from the gradient descent method used in backprop.

This problem is often expressed in geophysical neural application. The very large dataset, and sending each channel (attribute, input) back can significantly decreased learning rate (slow processing) and paralyze the network.

Resilient Propagation Algorithm (rProp)– one of the often improvements of backprop. The main difference is using only of partial derivations in process of weighting coefficient adjustment. It is about 4-5 times faster than the standard backprop algorithm.

Radial Basis Function Algorithm (RBF) – is an artificial network that uses radial basis fnction as activation function. Very often it is applied in function approximation, time series prediction etc.

A radial basis function is a real-valued function whose value depends only on the distance from the origin or alternatively on the distance from some other point c, called a center.

The Okoli field behaviour through, located in the Sava depression, is selected as the example for clastic facies prediction using neural network. The significant oil and gas reserves are proved in Lower Pontian sandstones.

The neural network was trained based on selected part of input data and registered lithology from c2 reservoir (as analytical target) of Lower Pontian age. Positions of facies (sand/marl sequences) were predicted.

The results indicate on over-trained network in the case of sandstone sequences prediction (Figures 10, 11), because the marl sequences in the top and the base are mostly replaced by sandstone.

The further neural facies modelling in the Sava depression need to be expanded with additional logs that characterised lithology and saturation (SP, CN, DEN).

Then, rPORP algorithm could be reached with more than 90% probability of true prediction (in presented analysis this value reached 82.1%).

This is the first neural analysis in hydrocarbon reservoir analysis in Croatia

Excellent correlation was obtained between predicted and true position of sandstone lithology (reservoir of Lower Pontian age in the Sava depression);

2. On contrary, positions of predicted and true marlstones positions (in top and bottom) mostly do not correspond;

3. The best prediction (so called Face machine) is reached in relatively early training period. In B-1 well such prediction is observed in 2186th iteration, and in B-2 well in 7626th iteration;

4. It means that in similar facies analyses in the Sava depression, it is not necessary to use large iteration set (here is used about 30000);

5. The input dataset would need to be extended on other log curves that characterize lithology, porosity and saturation, like SP (spontaneous potential), CN (compensated neutron), DEN (density) and some other;

6. The wished true prediction could reached 90% (Face machine could be configured with 90% probability).